Harbour porpoises produce distinct narrow band high frequency clicks centered around 130 kHz. Typically these are usually easy to identify by a manual analyst and should be relatively easy to automatically identify because there are few confounding sounds at those high frequencies. However, as with all automated analysis, it's not quite that simple; off-axis clicks, reflections causing distortion, noise from boat engines and low SNR clicks can be difficult for automated classifiers to cope with, leading to false positive and false negatives.
There are huge manually annotated datasets of porpoise clicks collected over decades - so a generic automated classifier seems like a prime candidate for a CNN or other deep learning approach. Does anyone know of or is working on a porpoise click classifier based on deep learning methods?